bioRxiv ScienceSearch

bioRxiv · 10.1101/751867

A data-based guide to the North American ecology faculty job market

Abstract

Every year for three years (2016 to 2018), I tried to identify every single person hired as a tenure track prof in ecology or an allied field (e.g., fish & wildlife) in N. America. I identified a total of 566 hires. I used public sources to compile various data on the new hires and the institutions that hired them (e.g., number of publications, teaching experience, hiring institution Carnegie class). I also compiled data provided by anonymous ecology faculty job seekers on ecoevojobs.net (e.g., number of positions applied for, number of publications, numbers of interviews and offers). And I polled readers of the Dynamic Ecology blog to get information about applicant and search committee behavior (e.g., regarding customization of applications to the hiring institution). These data address some widespread anxieties and misunderstandings about the ecology faculty job market, and also speak to gender diversity and equity in recent ecology faculty hiring. They complement, and in some cases improve on, other sources of information, such as anecdotal personal experiences.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fox, J.. 2019-09-07. A data-based guide to the North American ecology faculty job market. https://doi.org/10.1101/751867

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education

Evaluation of Reproducible and Transparent Research Practices in Sports Medicine Research: A Cross-sectional study

BackgroundIn recent years, urgency has been placed on the \"reproducibility crisis\" facing biomedical research. Despite efforts toward improvement, certain elements needed to reproduce a study are often lacking from publications. The current state of reproducibility within the sports medicine research community remains unknown.\n\nPurposeOur study sought to evaluate the presence of eight indicators of reproducibility and transparency to determine the current state of research reporting in sports medicine research.\n\nStudy DesignCross-sectional review\n\nMethodsUsing the National Library of Medicine catalog, we identified 41 MEDLINE-indexed, English language sports medicine journals. From the 41 journals, we randomly sampled 300 publications that were recorded on PubMed as being published between January 1, 2014, and December 31, 2018. Two investigators extracted data in duplicate and blinded fashion.\n\nResultsOf the 300 publications sampled, 280 were accessible and were screened for empirical data. Studies that lack empirical data were excluded from our analysis. Of the remaining 195 with empirical data, 10 (5.13%) publications provided data availability statements, 1 (0.51%) provided a protocol, 0 (0.0%) provided an analysis script, and 9 (4.62%) were pre registered.\n\nConclusionReproducibility and transparency indicators are lacking in sports medicine publications. The majority of publications lack the necessary resources for reproducibility such as material, data, analysis scripts, or protocol availability. While the current state of reproducibility cannot be fixed overnight, we feel combined efforts of data sharing, open access, and verifying disclosure statements can help to improve overall reporting.

scientific communication and education

Evaluation of Reproducibility in Urology Publications

Take Home MessageMany components of transparency and reproducibility are lacking in urology publications, making study replication, at best, difficult.\n\nIntroductionReproducibility is essential for the integrity of scientific research. Reproducibility is measured by the ability of investigators to replicate the outcomes of an original publication by using the same materials and procedures.\n\nMethodsWe sampled 300 publications in the field of urology for assessment of multiple indicators of reproducibility, including material availability, raw data availability, analysis script availability, pre-registration information, links to protocols, and whether the publication was freely available to the public. Publications were also assessed for statements about conflicts of interest and funding sources.\n\nResultsOf the 300 sample publications, 171 contained empirical data and could be analyzed for reproducibility. Of the analyzed articles, 0.58% (1/171) provided links to protocols, and none of the studies provided analysis scripts. Additionally, 95.91% (164/171) did not provide accessible raw data, 97.53% (158/162) did not provide accessible materials, and 95.32% (163/171) did not state they were pre-registered.\n\nConclusionCurrent urology research does not consistently provide the components needed to reproduce original studies. Collaborative efforts from investigators and journal editors are needed to improve research quality, while minimizing waste and patient risk.

scientific communication and education